Pemantauan terhadap parameter keluaran panel surya sangat perlu dilakukan untuk menilai kinerja sebuah panel surya pada kondisi lingkungan yang nyata. Paper ini bertujuan memberikan suatu teknik baru pemantauan secara langsung dan real time terhadap parameter keluaran dari sebuah panel surya yaitu tegangan dan arus pada kondisi lingkungan tertentu. Untuk memenuhi keperluan pemantauan tersebut, sistem pemantauan kinerja panel surya yang dirancang dilengkapi dengan sensor pengukur arus dan tegangan yang telah dikalibrasi, sistem akuisisi data yang diintegrasikan ke spreadsheet Excel menggunakan program aplikasi PLX-DAQ dan kartu memori sebagai penyimpan data cadangan. Perancangan sistem berbasis mikrokontroler Arduino Atmega 328P dan dihubungkan ke komputer melalui port serial RS232. Kelebihan dari sistem pemantauan ini adalah hasil pengukuran dari setiap sensor dapat diproses secara langsung dan ditampilkan dalam bentuk grafik pada kondisi real time. Informasi mengenai tegangan dan arus dari panel surya yang dikumpulkan pada kondisi real time dapat diperoleh langsung melalui spreadsheet Excel tanpa memerlukan program ulang terhadap Arduino. Fasilitas ini memberikan kemudahan untuk pengolahan data selanjutnya.

No abstract

In recent years, cross spectral matching has been gaining attention in various biometric systems for identification and verification purposes. Cross spectral matching allows images taken under different electromagnetic spectrums to match each other. In cross spectral matching, one of the keys for successful matching is determined by the features used for representing an image. Therefore, the feature extraction step becomes an essential task. Researchers have improved matching accuracy by developing robust features. This paper presents most commonly selected features used in cross spectral matching. This survey covers basic concepts of cross spectral matching, visual and thermal features extraction, and state of the art descriptors. In the end, this paper provides a description of better feature selection methods in cross spectral matching.

Early detection of plant diseases is one of the main keys to handling diseases quickly and successfully. The purpose of this study is to find out a simpler CNN architecture and meet an acceptable compromise between accuracy and simplification to detect diseases in tomato plants based on leaf images. This simpler architecture will allow the development of standalone and independent system model in the field to classify and identify the tomato plants diseases in low price and limited resources. This proposed architecture was developed from the CNN architecture baseline and is intended to classify 10 classes of tomato leaves consist of one healthy class and 9 classes of leaves diseases taken from the Plant Village dataset. In this study, the performance of the proposed architecture and comparative architectures are examined in the same dataset. Comparative architectures used are some existing CNN architectures that are commonly used namely VGG Net, Shuffle Net and Squeeze Net. The results indicated that the proposed architecture can achieve competitive accuracy compared with the existing architecture while the proposed architecture is much shorter than the existing architecture and better in terms of performance time.

Cross-spectral iris recognition represents the ability of the system to identify the iris images acquired in different electromagnetic spectrums. An iris captured in the near-infrared spectrum (NIR) is matched with an iris obtained in the visual light spectrum (VIS) to boost the recognition performance. In cross-spectral iris recognition, the illumination factor between NIR and VIS images significantly degrades the recognition performance. Therefore, the existing method only achieved recognition performance with an equal error rate (EER) larger than 5%, and it is a challenging issue for cross-spectral performance to have EER below 5%. In this paper, we improve iris recognition performance by concatenating the Gradientfacesbased normalization technique (GRF) to a standard (conventional) iris recognition method to alleviate the illumination effect. In addition, we integrate the GRF with a Gabor filter, a difference of Gaussian (DoG) filter, and texture descriptors, namely a binary statistical image feature (BSIF) and a local binary pattern (LBP). The experimental results show that the GRF can boost the cross-spectral iris recognition performance with an EER equals to 1.69%. In addition, the best cross-spectral iris recognition performance is achieved when the GRF is integrated with the Gabor filter and the BSIF.